Fastbook notebook(3 & 4)

-

Basics of deep learning

Lecture 3 is about data ethics, It's self Explanatory and Rachel’s Lecture is more than sufficient. 

Lecture 4 is more about breaking the myth behind neural networks. And the basics of pytorch and how fastai api makes deep learning cool.

You may come across jargon like Sigmoid, Relu, Stochastic Gradient Descent, Learning rate and so on. Their explanation is great. They explained maths with a piece of code and I completely enjoyed it.

What i would suggest is play with notebook 4 and learn more things. In case you don't understand something, go through it again and again and you will catch up. If you want some help ask the question in the forum.

You can practise lecture 4 by downloading the repository and going to the clean folder, there is only code not with a prose. It's the best place to practise what you have learned till now.

The motto is, If you are in doubt, run the code.

Comments

Post a Comment

Popular posts from this blog

Best practice to follow on git and github

-
Some basics: linux Note: <> just used to differentiate the file for understanding : ls -list the file mkdir <folder name> - creach the file name touch <file name> - create the file code . - take to vs code vi <filename >- open the vim editor - Don’t dive deep into vim editor There are two mode Edit mode and command mode You can press Esc to enter cmd mode to give command like insert delete update and so on  i - for insert a text in a file O, o move the cursor above and below :wq - write and save the file These are the basic about vim editor but don’t spend too much time on this use other editor for faster progress and so on We can directly open the file and made the changes Vim is fun to learn cat <file name > - show what are there in that file rm -rf <file/folder name> -remove the the file or folder from the current directory cd - change the directory(where we are working and our file path) cd .. go back the previous folder in a directory Git confi
Read more

Deep learning with TensorFlow

-
Image
After learning the Sci-kit learn library, I started to think What, Where,  How should I start learning deep learning which library should go on, I find myself Tensorflow is easy to begin with because TensorFlow is developed by google and they providing a stable and extensive documentation and so on... While learning Image classification I came to know the power of GPU, Everything comes in colab which is very much handy. Main thing I learned about deeplearning is You can do anything as long as you can convert into object into Tensors (Array of numbers) Classification of 101 image of food data image data is done with EfficientnetB0 model using transfer learning. There I, learned the power of Transfer Learning. TensorflowHub providing various Pretrained model, we can use based on our need.  And there is something called Tensorboard whwere we can the evaluate the various model, we build with deeplearning. I attached the sample image of Tensorboard.  And there are so many resource in the in
Read more

Image classification(Fastbook notebook_2)

-
Image
Hi Friends, What i am so excited about notebook2 is we are going to deploy a model, which helps to understand the flow of deep learning. I am going to discuss the problem I faced, may be you may face in your journey. First and foremost thing is you may not be able to get the bing api. If you are getting it, It's ok but if you face any difficulties means use DuckDuck go api which comes very much handy. Once you downloaded the dataset Everything worked fine until the deployment part.  What I would suggest is if you are using the colab like me means you may face difficulties in using viola. What I will suggest is to go for Gradio which is much easier. With an few line of code you may built a amazing application. My project is about classication of great ape(Chimpanzee, Bonobos and Gorilla) eg: In case you may face any difficulties go through my notebook . Feel free to ask the doubts.
Read more